Overview

Dataset statistics

Number of variables12
Number of observations188
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.4 KiB
Average record size in memory127.3 B

Variable types

Categorical1
Numeric11

Alerts

Date has a high cardinality: 188 distinct values High cardinality
Confirmed is highly correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Active is highly correlated with Confirmed and 7 other fieldsHigh correlation
New cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
New deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
New recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Cases is highly correlated with New deaths and 1 other fieldsHigh correlation
Recovered / 100 Cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Recovered is highly correlated with Recovered / 100 CasesHigh correlation
No. of countries is highly correlated with Confirmed and 8 other fieldsHigh correlation
Confirmed is highly correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 7 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 6 other fieldsHigh correlation
Active is highly correlated with Confirmed and 7 other fieldsHigh correlation
New cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
New deaths is highly correlated with Confirmed and 6 other fieldsHigh correlation
New recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Cases is highly correlated with New deaths and 1 other fieldsHigh correlation
Recovered / 100 Cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Recovered is highly correlated with Recovered / 100 Cases and 1 other fieldsHigh correlation
No. of countries is highly correlated with Confirmed and 9 other fieldsHigh correlation
Confirmed is highly correlated with Deaths and 6 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 6 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 6 other fieldsHigh correlation
Active is highly correlated with Confirmed and 6 other fieldsHigh correlation
New cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
New deaths is highly correlated with New cases and 1 other fieldsHigh correlation
New recovered is highly correlated with Confirmed and 6 other fieldsHigh correlation
Deaths / 100 Cases is highly correlated with New deathsHigh correlation
Recovered / 100 Cases is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Recovered is highly correlated with Recovered / 100 CasesHigh correlation
No. of countries is highly correlated with Confirmed and 6 other fieldsHigh correlation
Confirmed is highly correlated with Deaths and 7 other fieldsHigh correlation
Deaths is highly correlated with Confirmed and 9 other fieldsHigh correlation
Recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Active is highly correlated with Confirmed and 9 other fieldsHigh correlation
New cases is highly correlated with Confirmed and 9 other fieldsHigh correlation
New deaths is highly correlated with Confirmed and 8 other fieldsHigh correlation
New recovered is highly correlated with Confirmed and 7 other fieldsHigh correlation
Deaths / 100 Cases is highly correlated with Confirmed and 9 other fieldsHigh correlation
Recovered / 100 Cases is highly correlated with Confirmed and 9 other fieldsHigh correlation
Deaths / 100 Recovered is highly correlated with Deaths and 5 other fieldsHigh correlation
No. of countries is highly correlated with Deaths and 6 other fieldsHigh correlation
Date is uniformly distributed Uniform
Date has unique values Unique
Confirmed has unique values Unique
Deaths has unique values Unique
Recovered has unique values Unique
Active has unique values Unique
New cases has unique values Unique
New recovered has unique values Unique

Reproduction

Analysis started2022-06-04 13:48:35.483071
Analysis finished2022-06-04 13:49:11.679151
Duration36.2 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
2020-05-07
 
1
2020-07-20
 
1
2020-06-24
 
1
2020-03-23
 
1
2020-03-16
 
1
Other values (183)
183 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)100.0%

Sample

1st row2020-01-22
2nd row2020-01-23
3rd row2020-01-24
4th row2020-01-25
5th row2020-01-26

Common Values

ValueCountFrequency (%)
2020-05-071
 
0.5%
2020-07-201
 
0.5%
2020-06-241
 
0.5%
2020-03-231
 
0.5%
2020-03-161
 
0.5%
2020-07-241
 
0.5%
2020-04-021
 
0.5%
2020-02-071
 
0.5%
2020-05-111
 
0.5%
2020-03-301
 
0.5%
Other values (178)178
94.7%

Length

2022-06-04T19:19:11.904954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-05-311
 
0.5%
2020-05-141
 
0.5%
2020-04-101
 
0.5%
2020-03-041
 
0.5%
2020-02-111
 
0.5%
2020-02-211
 
0.5%
2020-03-131
 
0.5%
2020-02-051
 
0.5%
2020-07-071
 
0.5%
2020-03-111
 
0.5%
Other values (178)178
94.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Confirmed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4406960.011
Minimum555
Maximum16480485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:12.014951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum555
5-th percentile10665.85
Q1112191
median2848733
Q37422045.5
95-th percentile14209025.75
Maximum16480485
Range16479930
Interquartile range (IQR)7309854.5

Descriptive statistics

Standard deviation4757988.322
Coefficient of variation (CV)1.079653165
Kurtosis-0.2842619958
Mean4406960.011
Median Absolute Deviation (MAD)2770604.5
Skewness0.9280697239
Sum828508482
Variance2.263845288 × 1013
MonotonicityStrictly increasing
2022-06-04T19:19:12.144952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61855301
 
0.5%
922411
 
0.5%
65209241
 
0.5%
797071
 
0.5%
238981
 
0.5%
1460081
 
0.5%
3837501
 
0.5%
99555971
 
0.5%
138125251
 
0.5%
20660031
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
5551
0.5%
6541
0.5%
9411
0.5%
14341
0.5%
21181
0.5%
29271
0.5%
55781
0.5%
61661
0.5%
82341
0.5%
99271
0.5%
ValueCountFrequency (%)
164804851
0.5%
162517961
0.5%
160471901
0.5%
157916451
0.5%
155104811
0.5%
152277251
0.5%
149470781
0.5%
147136231
0.5%
145068451
0.5%
142921981
0.5%

Deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230770.7606
Minimum17
Maximum654036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:12.280950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile229.1
Q13935
median204190
Q3418634.5
95-th percentile600160.55
Maximum654036
Range654019
Interquartile range (IQR)414699.5

Descriptive statistics

Standard deviation217929.0942
Coefficient of variation (CV)0.9443531476
Kurtosis-1.311583989
Mean230770.7606
Median Absolute Deviation (MAD)200792
Skewness0.378594074
Sum43384903
Variance4.749309009 × 1010
MonotonicityStrictly increasing
2022-06-04T19:19:12.406933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88451
 
0.5%
2763041
 
0.5%
11181
 
0.5%
3618201
 
0.5%
13711
 
0.5%
11131
 
0.5%
1739651
 
0.5%
869151
 
0.5%
2486591
 
0.5%
821
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
171
0.5%
181
0.5%
261
0.5%
421
0.5%
561
0.5%
821
0.5%
1311
0.5%
1331
0.5%
1711
0.5%
2131
0.5%
ValueCountFrequency (%)
6540361
0.5%
6486211
0.5%
6445171
0.5%
6396501
0.5%
6335061
0.5%
6235401
0.5%
6165571
0.5%
6103191
0.5%
6061591
0.5%
6021301
0.5%

Recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2066001.218
Minimum28
Maximum9468087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:12.542917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile240.7
Q160441.25
median784784
Q33416395.75
95-th percentile7891773.5
Maximum9468087
Range9468059
Interquartile range (IQR)3355954.5

Descriptive statistics

Standard deviation2627976.395
Coefficient of variation (CV)1.272011058
Kurtosis0.4548214563
Mean2066001.218
Median Absolute Deviation (MAD)780718.5
Skewness1.266406341
Sum388408229
Variance6.906259933 × 1012
MonotonicityStrictly increasing
2022-06-04T19:19:12.672919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
266521
 
0.5%
43668751
 
0.5%
7982391
 
0.5%
218491
 
0.5%
40888261
 
0.5%
91587431
 
0.5%
23637461
 
0.5%
1114451
 
0.5%
21806051
 
0.5%
71811391
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
281
0.5%
301
0.5%
361
0.5%
391
0.5%
521
0.5%
611
0.5%
1071
0.5%
1251
0.5%
1411
0.5%
2191
0.5%
ValueCountFrequency (%)
94680871
0.5%
92934641
0.5%
91587431
0.5%
89397051
0.5%
87109691
0.5%
85412551
0.5%
83649861
0.5%
81907771
0.5%
80322351
0.5%
79445501
0.5%

Active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2110188.032
Minimum510
Maximum6358362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:12.802917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile10196.05
Q158641.75
median1859759
Q33587015.25
95-th percentile5717091.7
Maximum6358362
Range6357852
Interquartile range (IQR)3528373.5

Descriptive statistics

Standard deviation1969670.45
Coefficient of variation (CV)0.9334099236
Kurtosis-1.002192822
Mean2110188.032
Median Absolute Deviation (MAD)1800910
Skewness0.5042073597
Sum396715350
Variance3.87960168 × 1012
MonotonicityNot monotonic
2022-06-04T19:19:12.928986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
995831
 
0.5%
287501
 
0.5%
20014951
 
0.5%
33124251
 
0.5%
581971
 
0.5%
41408841
 
0.5%
14851091
 
0.5%
49644471
 
0.5%
47550721
 
0.5%
59125271
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
5101
0.5%
6061
0.5%
8791
0.5%
13531
0.5%
20101
0.5%
27841
0.5%
53401
0.5%
59081
0.5%
79221
0.5%
94951
0.5%
ValueCountFrequency (%)
63583621
0.5%
63097111
0.5%
62439301
0.5%
62122901
0.5%
61660061
0.5%
60629301
0.5%
59655351
0.5%
59125271
0.5%
58684511
0.5%
57455181
0.5%

New cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87771.02128
Minimum0
Maximum282756
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:13.133957image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile648.25
Q15568.5
median81114
Q3131502.5
95-th percentile230271.85
Maximum282756
Range282756
Interquartile range (IQR)125934

Descriptive statistics

Standard deviation75295.29326
Coefficient of variation (CV)0.857860512
Kurtosis-0.4123708006
Mean87771.02128
Median Absolute Deviation (MAD)61133.5
Skewness0.6271265148
Sum16500952
Variance5669381186
MonotonicityNot monotonic
2022-06-04T19:19:13.251955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
873351
 
0.5%
1022251
 
0.5%
1623491
 
0.5%
2176891
 
0.5%
2335651
 
0.5%
31591
 
0.5%
858461
 
0.5%
8531
 
0.5%
64841
 
0.5%
2420381
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
01
0.5%
991
0.5%
2871
0.5%
3231
0.5%
4211
0.5%
4931
0.5%
5471
0.5%
5641
0.5%
5881
0.5%
6291
0.5%
ValueCountFrequency (%)
2827561
0.5%
2811641
0.5%
2806471
0.5%
2555451
0.5%
2525441
0.5%
2420381
0.5%
2376351
0.5%
2335651
0.5%
2325771
0.5%
2311221
0.5%

New deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3478.824468
Minimum0
Maximum9966
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:13.380943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.2
Q1250.75
median4116
Q35346
95-th percentile6887.15
Maximum9966
Range9966
Interquartile range (IQR)5095.25

Descriptive statistics

Standard deviation2537.735652
Coefficient of variation (CV)0.729480799
Kurtosis-1.166446347
Mean3478.824468
Median Absolute Deviation (MAD)1740
Skewness-0.1261654422
Sum654019
Variance6440102.242
MonotonicityNot monotonic
2022-06-04T19:19:13.509917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002
 
1.1%
34732
 
1.1%
53112
 
1.1%
801
 
0.5%
951
 
0.5%
48271
 
0.5%
931
 
0.5%
891
 
0.5%
3441
 
0.5%
871
 
0.5%
Other values (175)175
93.1%
ValueCountFrequency (%)
01
0.5%
11
0.5%
21
0.5%
41
0.5%
51
0.5%
81
0.5%
101
0.5%
141
0.5%
161
0.5%
261
0.5%
ValueCountFrequency (%)
99661
0.5%
88901
0.5%
83121
0.5%
79021
0.5%
76291
0.5%
72831
0.5%
72721
0.5%
71571
0.5%
69831
0.5%
68981
0.5%

New recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct188
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50362.01596
Minimum0
Maximum284394
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:13.634953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67.6
Q12488.25
median30991.5
Q379706.25
95-th percentile159114.65
Maximum284394
Range284394
Interquartile range (IQR)77218

Descriptive statistics

Standard deviation56090.89248
Coefficient of variation (CV)1.113753916
Kurtosis1.471401478
Mean50362.01596
Median Absolute Deviation (MAD)29331.5
Skewness1.303823696
Sum9468059
Variance3146188219
MonotonicityNot monotonic
2022-06-04T19:19:13.757916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1077731
 
0.5%
228531
 
0.5%
123671
 
0.5%
781
 
0.5%
1342211
 
0.5%
1063161
 
0.5%
5871
 
0.5%
630501
 
0.5%
581851
 
0.5%
54481
 
0.5%
Other values (178)178
94.7%
ValueCountFrequency (%)
01
0.5%
21
0.5%
31
0.5%
61
0.5%
91
0.5%
131
0.5%
161
0.5%
181
0.5%
461
0.5%
621
0.5%
ValueCountFrequency (%)
2843941
0.5%
2287361
0.5%
2190381
0.5%
1954331
0.5%
1762691
0.5%
1746231
0.5%
1742091
0.5%
1697141
0.5%
1595191
0.5%
1594231
0.5%

Deaths / 100 Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct162
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.860638298
Minimum2.04
Maximum7.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:13.887950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2.04
5-th percentile2.1635
Q13.51
median4.85
Q36.2975
95-th percentile7.11
Maximum7.18
Range5.14
Interquartile range (IQR)2.7875

Descriptive statistics

Standard deviation1.579540858
Coefficient of variation (CV)0.324965727
Kurtosis-1.149221999
Mean4.860638298
Median Absolute Deviation (MAD)1.41
Skewness-0.1445400484
Sum913.8
Variance2.494949323
MonotonicityNot monotonic
2022-06-04T19:19:14.018917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.434
 
2.1%
7.183
 
1.6%
7.053
 
1.6%
3.443
 
1.6%
3.152
 
1.1%
5.282
 
1.1%
7.142
 
1.1%
4.482
 
1.1%
3.412
 
1.1%
6.882
 
1.1%
Other values (152)163
86.7%
ValueCountFrequency (%)
2.041
0.5%
2.062
1.1%
2.081
0.5%
2.091
0.5%
2.141
0.5%
2.152
1.1%
2.162
1.1%
2.171
0.5%
2.261
0.5%
2.282
1.1%
ValueCountFrequency (%)
7.183
1.6%
7.162
1.1%
7.151
 
0.5%
7.142
1.1%
7.131
 
0.5%
7.112
1.1%
7.091
 
0.5%
7.071
 
0.5%
7.053
1.6%
7.041
 
0.5%

Recovered / 100 Cases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.34393617
Minimum1.71
Maximum57.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:14.147947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.71
5-th percentile3.18
Q122.785
median35.68
Q348.945
95-th percentile55.422
Maximum57.45
Range55.74
Interquartile range (IQR)26.16

Descriptive statistics

Standard deviation16.20615851
Coefficient of variation (CV)0.4718783087
Kurtosis-0.8430659039
Mean34.34393617
Median Absolute Deviation (MAD)13.205
Skewness-0.4312834599
Sum6456.66
Variance262.6395737
MonotonicityNot monotonic
2022-06-04T19:19:14.273945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.12
 
1.1%
53.592
 
1.1%
20.032
 
1.1%
2.731
 
0.5%
36.041
 
0.5%
20.41
 
0.5%
45.961
 
0.5%
30.591
 
0.5%
12.951
 
0.5%
18.351
 
0.5%
Other values (175)175
93.1%
ValueCountFrequency (%)
1.711
0.5%
1.921
0.5%
2.031
0.5%
2.081
0.5%
2.211
0.5%
2.331
0.5%
2.461
0.5%
2.721
0.5%
2.731
0.5%
3.041
0.5%
ValueCountFrequency (%)
57.451
0.5%
57.181
0.5%
57.071
0.5%
56.611
0.5%
56.161
0.5%
56.091
0.5%
55.961
0.5%
55.671
0.5%
55.591
0.5%
55.451
0.5%

Deaths / 100 Recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct182
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.10452128
Minimum6.26
Maximum134.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:14.401953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6.26
5-th percentile6.98
Q19.65
median15.38
Q325.3425
95-th percentile71.6285
Maximum134.43
Range128.17
Interquartile range (IQR)15.6925

Descriptive statistics

Standard deviation22.56830709
Coefficient of variation (CV)1.020981491
Kurtosis9.795616761
Mean22.10452128
Median Absolute Deviation (MAD)7.105
Skewness3.042262805
Sum4155.65
Variance509.3284848
MonotonicityNot monotonic
2022-06-04T19:19:14.543944image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107.692
 
1.1%
27.282
 
1.1%
11.982
 
1.1%
6.982
 
1.1%
28.762
 
1.1%
8.232
 
1.1%
7.31
 
0.5%
59.931
 
0.5%
18.731
 
0.5%
28.191
 
0.5%
Other values (172)172
91.5%
ValueCountFrequency (%)
6.261
0.5%
6.361
0.5%
6.41
0.5%
6.431
0.5%
6.541
0.5%
6.551
0.5%
6.761
0.5%
6.781
0.5%
6.911
0.5%
6.982
1.1%
ValueCountFrequency (%)
134.431
0.5%
122.431
0.5%
121.281
0.5%
107.692
1.1%
106.41
0.5%
97.261
0.5%
92.171
0.5%
78.871
0.5%
72.221
0.5%
70.531
0.5%

No. of countries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.3510638
Minimum6
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2022-06-04T19:19:14.682968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile24.35
Q1101.25
median184
Q3187
95-th percentile187
Maximum187
Range181
Interquartile range (IQR)85.75

Descriptive statistics

Standard deviation65.17597856
Coefficient of variation (CV)0.4515102059
Kurtosis-0.498936159
Mean144.3510638
Median Absolute Deviation (MAD)3
Skewness-1.138638031
Sum27138
Variance4247.908181
MonotonicityIncreasing
2022-06-04T19:19:14.810951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18776
40.4%
18420
 
10.6%
18613
 
6.9%
2610
 
5.3%
275
 
2.7%
1834
 
2.1%
1763
 
1.6%
253
 
1.6%
1803
 
1.6%
302
 
1.1%
Other values (46)49
26.1%
ValueCountFrequency (%)
61
 
0.5%
81
 
0.5%
91
 
0.5%
111
 
0.5%
131
 
0.5%
162
1.1%
181
 
0.5%
201
 
0.5%
241
 
0.5%
253
1.6%
ValueCountFrequency (%)
18776
40.4%
18613
 
6.9%
18420
 
10.6%
1834
 
2.1%
1821
 
0.5%
1803
 
1.6%
1792
 
1.1%
1771
 
0.5%
1763
 
1.6%
1751
 
0.5%

Interactions

2022-06-04T19:19:09.516768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:56.292396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.701553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.607769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.796773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.033767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.306768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.574768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.793787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.050767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.252768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.633766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:56.541557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.836556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.720770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.913769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.155768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.424769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.689769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.908768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.162768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.377769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.833768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:56.664567image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.960557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.827769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.030769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.263768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.554768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.800768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.021768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.271768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.499768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.941768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:56.775572image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:58.065557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.923776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.138769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.362768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.671771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.904769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.124768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.373768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.604768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.090771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:56.891557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:58.179581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.028768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.249767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.469768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.787769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.009768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.318769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.480768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.715768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.232769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.000557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:58.288557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.135771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.356768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.652768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.894778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.116803image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.419768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.583768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.825768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.345768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.115557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.042769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.236769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.463769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.772768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.002768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.222767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.524769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.692767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.934769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.461780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.226574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.147768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.342769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.576769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.874767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.116768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.333768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.624768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.800769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.045768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.574769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.347558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.252768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.446768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.690766image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:02.977768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.229769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.448768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.730767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:07.903769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.158788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.690768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.456588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.363782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.559788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.804770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.085768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.336768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.563768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.833770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.011769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.270768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:10.810777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:57.573588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:18:59.486769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:00.682771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:01.929768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:03.195768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:04.455767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:05.677768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:06.941768image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:08.132767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2022-06-04T19:19:09.388772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2022-06-04T19:19:14.930951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-04T19:19:15.314951image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-04T19:19:15.523953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-04T19:19:15.735948image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-04T19:19:11.037820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-04T19:19:11.280773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredNo. of countries
02020-01-2255517285100003.065.0560.716
12020-01-23654183060699122.754.5960.008
22020-01-249412636879287862.763.8372.229
32020-01-251434423913534931632.932.72107.6911
42020-01-2621185652201068414132.642.46107.6913
52020-01-272927826127848092692.802.08134.4316
62020-01-2855781311075340265149462.351.92122.4316
72020-01-29616613312559085882182.162.03106.4018
82020-01-3082341711417922206838162.081.71121.2820
92020-01-3199272132199495169342782.152.2197.2624

Last rows

DateConfirmedDeathsRecoveredActiveNew casesNew deathsNew recoveredDeaths / 100 CasesRecovered / 100 CasesDeaths / 100 RecoveredNo. of countries
1782020-07-18142921986021307944550574551823763556271507904.2155.597.58187
1792020-07-1914506845606159803223558684512146474029876854.1855.377.55187
1802020-07-20147136236103198190777591252720677841601585424.1555.677.45187
1812020-07-21149470786165578364986596553523356562381742094.1255.967.37187
1822020-07-22152277256235408541255606293028064769831762694.0956.097.30187
1832020-07-23155104816335068710969616600628275699661697144.0856.167.27187
1842020-07-24157916456396508939705621229028116461442287364.0556.617.16187
1852020-07-25160471906445179158743624393025554548672190384.0257.077.04187
1862020-07-26162517966486219293464630971120460641041347213.9957.186.98187
1872020-07-27164804856540369468087635836222869354151746233.9757.456.91187